WhatA tool that acts as a skeptical second opinion on AI-generated security assessments, specifically designed to catch cases where models falsely claim code is safe.
SignalExperienced security engineers report that LLMs confidently declare code secure when it isn't — stating security boundaries exist when they don't — and that this false confidence is dangerous because teams may trust the AI's assessment and stop looking.
Why NowTeams are rapidly adopting AI code review and security scanning, but the models' tendency to hallucinate safety creates a new category of risk that didn't exist before AI-assisted development became mainstream.
MarketEngineering teams using AI for security review (~$2B code security tools market); competes with Snyk and GitHub Advanced Security but addresses the novel problem of AI false assurance rather than traditional static analysis.
MoatA curated dataset of real-world cases where AI security assessments were wrong, used to train adversarial verification models, creates a unique data moat that improves with every customer engagement.
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